Hadoop is on the top of the list of
technologies used for dealing with Big data due to its ultra high scalability
& low cost compared to other platforms.It is a suite of products linked together, which breaks up the large
datasets into smaller chunks on commodity servers, and data processing is done
in a distributed cluster environment to quickly return the results.

Because of nascent stage immaturity of Big
data initiatives, there are many views of what is it & how it can be
applied.Organizations need to focus on Big
data processing, while avoiding the movement of large volumes of data which is
very costly.

Big data help make better decisions – faster,
more efficiently with higher quality.

Netflix uses data mining to find out correlations between different movies that subscribers rent & then recommend the one which you are most likely to watch

ING using personalized campaign offers in real time by predicting who will respond, to increase 30-40% response rates & reduce direct marketing costs by 35% per year.

Amazon.com using price optimization based on demand to increase the online shopping revenues.

How various industries are using prescriptive analytics?

·Consumer product companies are using it to maximize the marketing dollar spend

·Transportation & Logistics companies are using it to find the best route for their deliveries & back haul

·Healthcare service providers are using it to decide how many beds they should increase in the hospitals

·Manufacturing giants are using it for inventory optimization to decide how much safety stock they should keep of each item, where to stock it based on the demand

·Telecom business are using it for providing on the spot offers to customers when they call the customer service centers

One daily life example - Imagine you are driving a car with a built in GPS, which analyses all the data it collects from the satellite about traffic, accidents, weather etc. It tells you which routes will have heavy traffic (prediction), but also recommends you the alternate routes (prescription) with less traffic.

So Prescriptive analytics is where you know what the future is, but also know what to do with it, with alternatives of best outcomes J

·Patient transportation services claiming charges for patients who are not even moved to and from hospitals/home

One industry example mentioned in the reports: In one brash scheme, immigrants set up a network of fraudulent medical-supply stores in the Southwest, hoping to cheat Medicaid and Medicare. The gang hired recruiters to bring them innocent patients eligible for Medicaid or Medicare. They then paid off local doctors to prescribe motorized wheelchairs worth $7,500 but instead gave them motor scooters worth just $1,500, pocketing the difference. Investigators shut down the scheme after noticing billings for wheelchairs in Arizona, Texas, and other states scaling into the hundreds of millions of dollars.

Sunday, 19 February 2012

As a customer, when you buy
any home appliances like TV, AC, Refrigerator, Home Theatre or a brand new car,
you get a company warranty along with it. This is the commitment from the
manufacturer that if any problem arises in the product or spare parts within
the warranty period, then company will repair or replace it free of cost.

Industry numbers shows that
warranty costs range from 2% to 6% of the company’s revenues.

Predicting these warranty
costs is an important step for successfully managing the business. If
manufacturers reserve too much money, then they lose opportunities to grow the
business, because they end up with less cash. If they set aside too
little money, then they lose opportunities because they have to keep adding to
the warranty reserves funds.

Let us see some quick
definitions of warranty:

Base
Warranty – original warranty coverage provided by manufacturer at no extra
cost, since it is included in the product price.

Extended
warranty – this comes into effect after the base warranty expires.

Warranty
reserves – amount of money set aside by manufacturer for the purpose of
servicing the warranty claims. This is based on the forecasted warranty
costs.

In automotive industry,
warranty generally guaranties free repairs or replacements subject to both age
of the car & mileage.

Warranty Analytics is
integration of warranty claims data with customer, product, sales and
geographic information, so companies can accelerate detection of failures and
reduce time to correction.

It can help in
significantly improve the early warnings of parts failures based on customer
complaints and failure patterns, combining structured data with un-structured
data (such as call center records) to give alerts and information about
developing trends that would have gone unnoticed earlier.

By identifying
warranty-related issues early, companies can save thousands of dollars in both
repair costs and customer retention because issues are proactively addressed
before they become significant, costly problems.

Root cause identification
of parts failures is the biggest challenge in the industry today. 70% of
annual warranty expenses are consumed by repetitive and chronic problems.
Prioritization of these root causes helps companies calculate how much it will
cost if nothing is done. This allows them to determine the best course of
action and associated costs, as well as any potential effect on customer
satisfaction.

What-if
analysis such as if we increase the mileage what will be impact on
warranty costs

Some of the warranty
analytics benefits:

Increased
customer satisfaction, product quality & brand reputation

Tremendous
impact on bottom line due to early issues identification

Huge
reductions in total manual claims processing costs

Prevention
of fraud on warranty claims

Optimized warranty
policies for maximum financial performance

Increase
efficiency of support logistics such as optimum stocking of replacement
parts or deployment of technicians

It helps answers the
questions like:

Our
competitors just raised their product warranty from 3 years to 6. If we do
adopt the same, how much more warranty costs we will incur? If we don't,
how much revenue we will we lose from reduced market share?

Given
a new product with no historical data, should we play it safe and offer
only a one year warranty, or can we offer a three year warranty to improve
our brand perception?

Sunday, 15 January 2012

Companies are trying to explore new markets while retaining their existing customers. This effort has acquired a new dimension with the explosion of big data and social media. In order to strategize faster and speed up the response to real-time or near real-time levels, swift analysis has become crucial.

Numerous factors are driving the adoption of in-memory analytics. Let us examine some of them...